Abstract

Many firms are outsourcing their information and computational needs because of the fast advancement of modern computing technology. Cloud-based computing systems must provide safeguards, including privacy, accessibility, and integrity, making a highly reliable platform crucial. Monitoring malware behavior throughout the whole characteristic spectrum significantly enhances security tactics compared to old methods. This research offers a novel method to improve the capacity of Cloud service suppliers to analyze users' behaviors. This research used a Particle Swarm Optimization-based Deep Learning Model the identification and optimization procedure. During recognition procedure, the system transformed users' behaviors into an understandable format and identified dangerous behaviors using multi-layer neural networks. The analysis of the experimental data indicates that the suggested approach is favorable for use in security surveillance and identification of hostile activities.

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